Normalizations

class vision_architectures.utils.normalizations.LayerNorm2D(normalized_shape, eps=1e-05, elementwise_affine=True, bias=True, device=None, dtype=None)

Bases: LayerNorm

forward(input)

Define the computation performed at every call.

Should be overridden by all subclasses. :rtype: Tensor

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class vision_architectures.utils.normalizations.LayerNorm3D(normalized_shape, eps=1e-05, elementwise_affine=True, bias=True, device=None, dtype=None)

Bases: LayerNorm

forward(input)

Define the computation performed at every call.

Should be overridden by all subclasses. :rtype: Tensor

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class vision_architectures.utils.normalizations.DyT(normalized_shape, alpha0=0.5)

Bases: Module

__init__(normalized_shape, alpha0=0.5)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(x)

Define the computation performed at every call.

Should be overridden by all subclasses. :rtype: Tensor

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

extra_repr()

Return the extra representation of the module.

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

Return type:

str

vision_architectures.utils.normalizations.get_norm_layer(normalization_name, *args, **kwargs)